Development of a Machine Learning derived Anticholinergic Burden Scale
(ML-ACB scale): A Machine Learning Approach with Enhanced Drug
Properties and Weighting
Abstract
Aim: This study aims to refine the anticholinergic burden (AB) scale
developed in our previous research by incorporating additional drug
properties, such as Lipophilicity and Molecular Weight, and implementing
a new weighting approach to address the varying influence of each drug
property on anticholinergic burden. The objective is to improve the
scale’s predictive accuracy and compare its performance against
established scales. Methods: The scale, which covers 87 drugs, was
expanded to include seven drug properties, combining new properties,
Lipophilicity and Molecular Weight, with previously utilised
experimental and in silico ADME, physicochemical, and pharmacological
properties. A weighting approach was introduced to the hierarchical
clustering process to account for the differential impact of each drug
property on AB. The performance of this revised scale was evaluated
through 10-fold cross-validation against the clinical Anticholinergic
Cognitive Burden (ACB) scale and the non-clinical Anticholinergic
Toxicity Scores (ATS) scale. Results: The scale showed improved
alignment with the ACB and ATS scales, agreeing with the rankings of 54
out of 87 drugs and 16 out of 25 drugs respectively. The Area Under the
Receiver Operating Characteristic Curve (AUROC) indicated strong
performance. The ML-ACB and ACB has an AUC of 0.99 and 0.81
respectively, whilst the ML-ACB and ATS had an AUC of 0.96 and 0.62.
Conclusion: The ML-ACB scale offers improved alignment with the
established ACB scale. This highlights the potential of the ML-ACB scale
as a valuable tool for clinical and research applications, providing a
data-driven alternative that closely correlates with existing validated
scales.